Method and system for providing sales information and insights through a conversational interface

ABSTRACT

A method and system are described that provide responses to natural language queries regarding the performance of a business. The method and system processes data from multiple data sources including information generated by the business and analyzes the data to provide actionable suggestions as to how to determine how to improve the performance of the business. The use of natural language queries allows for a merchant without a business intelligence background obtain these insights easily.

FIELD OF THE DISCLOSURE

The present disclosure relates to a method and system for providingsales information and insights to customers in response toconversational questions posed by users.

BACKGROUND

Small businesses have a wealth of information regarding their customers,supply chains, and other aspects of their day-to-day operations that arestored in a variety of locations. For example, transaction data entriesand payment transactions that have been cleared forwarded to paymentprocessors may include valuable information relevant to the optimizationof operations at the small business. In addition, other sources ofinformation exist that can help clarify the cause and effectrelationship between external conditions and changes in the performanceof the small business. Further, small businesses typically have someinformation stored in a merchant server that may include payments andpurchases that have not yet been forwarded to payment processors, orother types of information including customer payment preferences andcustomer delivery or pickup preferences. Examples of data that may beextracted from such information include market share data. U.S. patentapplication Ser. No. 14/498,194 entitled “Method and System forIdentifying Merchant Market Shares Based on Purchase Data” filed on Sep.26, 2014 describes one way to obtain market share data from theinformation available from businesses and is incorporated by referencein its entirety.

Although this corpus of information is being generated by a typicalsmall business, the resources are not ordinarily available for the smallbusiness to analyze this information to derive useful information thesmall business can use to improve their performance. Even if the smallbusiness has the resources to analyze this information and derive usefulinformation, the techniques used to derive the useful information arespecific to interpreting large volumes of various types of informationand may not be within the skills of a typical small business owner.Accordingly a technical problem exists where there is a very specificskill set is needed to properly extract actionable information from theinformation generated by a typical business.

There therefore exists a need to address this technical problem byproviding sales information and other insights through a conversationalinterface so that the small business owner can easily analyze the wealthof available information and derive useful information that can be usedto improve the performance of the small business. The conversationalinterface is of particular use because the small business owner need notknow specific terms of art needed to derive useful information and caninstead simply submit natural language queries to determine how toimprove the performance of their small business. The technical problemis therefore addressed by providing an easy to use interface that allowsfor actionable information to be extracted from the informationgenerated by a typical business.

SUMMARY

The present disclosure provides for a method of providing responses to anatural language query comprising receiving a natural language queryfrom a merchant, the merchant being associated with a merchantidentifier, processing the natural language query to derive at least oneintent and at least one entity, retrieving a plurality of transactiondata entries, each transaction data entry including transaction dataassociated with a payment transaction having the merchant identifierassociated with the merchant, identifying a set of the plurality oftransaction data entries that correspond to the at least one derivedintent or the at least one entity, calculating a response to the naturallanguage query based on the set of the plurality of transaction dataentries using at least one analytical model, generating a naturallanguage response, based on the calculated response, to the naturallanguage query, and transmitting the generated natural languageresponse.

The method may further comprise retrieving a plurality of externalinformation associated with the at least derived one intent or the atleast one entity from at least one external data source, and identifyinga set of the plurality of external information that correspond to the atleast one derived intent or the at least one entity. The calculatedresponse is further based on the set of the plurality of externalinformation. When the at least one derived intent includes a scheduledtask for monitoring the plurality of transaction data entries, thescheduled task has at least one triggering threshold where theidentified set of the plurality of transaction data entries is updatedbased on the triggering threshold, and where the calculated response isupdated based on the triggering threshold and the updated identified setof the plurality of transactions. When the derived intent is a requestfor explaining the generated natural language response, the at least oneanalytical model is transmitted. The method may further includeretrieving another plurality of transaction data entries where eachtransaction data entry includes transaction data associated with apayment transaction not having the merchant identifier associated withthe merchant. The calculated response may further be based on theanother plurality of external information. The calculated response mayremove identifying information from the another plurality of externalinformation. The at least one data source includes merchant accountingsoftware executing on a merchant server. The at least one derived intentincludes one of a performance metric, a comparison metric, a timeframe,and a target user.

The present disclosure also provides for a system providing responses toa natural language query comprising a computing device configured toinput into an interface a natural language query created by a merchant,and a processing server. The processing server includes a receivingdevice configured to receive the natural language query being input intothe computing device being used by the merchant, where the merchant isassociated with a merchant identifier, a natural language processorconfigured to derive at least one intent and at least one entity, aquerying module configured to retrieve a plurality of transaction dataentries where each transaction data entry including transaction dataassociated with a payment transaction having the merchant identifierassociated with the merchant and also further configured to identify aset of the plurality of transaction data entries that correspond to theat least one derived intent or the at least one entity. The processingserver further includes a calculation module configured to calculate aresponse to the natural language query based on the set of the pluralityof transaction data entries using at least one analytical model, anatural language response module configured to generate a naturallanguage response, based on the calculated response, to the naturallanguage query, and a transmitting device configured to transmit thegenerated natural language response.

The querying module is further configured to retrieve a plurality ofexternal information associated with the at least derived one intent orthe at least one entity from at least one external data source, andidentify a set of the plurality of external information that correspondto the at least one derived intent or the at least one entity. Thecalculation module is further configured to calculate the response basedon the set of the plurality of external information. When the naturallanguage processor derived intent includes a scheduled task formonitoring the plurality of transaction data entries, the scheduled taskhas at least one triggering threshold where the querying module updatesthe identified set of the plurality of transaction data entries based onthe triggering threshold, and the calculation module updates thecalculated response based on the triggering threshold and the updatedidentified set of the plurality of transactions. When the receivingdevice receives a request from the computing device of the merchant toexplain the generated natural language response and when the naturallanguage processor determines the derived intent is a request forexplaining the generated natural language response, the transmittingdevice transmits the at least one analytical model used by thecalculation module to generate the response on which the generatednatural language response is based. The querying module is furtherconfigured to retrieve another plurality of transaction data entrieswhere each transaction data entry including transaction data associatedwith a payment transaction not having the merchant identifier associatedwith the merchant, and where the calculation module calculated responseis further based on the another plurality of external information. Thequerying module is further configured to remove identifying informationfrom the another plurality of transaction data entries. The at least onedata source includes merchant accounting software executing on amerchant server. The at least one derived intent includes one of aperformance metric, a comparison metric, a timeframe, and a target user

BRIEF DESCRIPTION OF THE DRAWING FIGURES

FIG. 1 depicts a system for providing sales information and insightsthrough a conversational interface.

FIG. 2 depicts the processing server.

FIG. 3 depicts a method for providing sales information and insightsthrough a conversational interface.

FIG. 4 depicts the process of providing sales information and insightsthrough a conversational interface.

FIG. 5 illustrates a method for providing sales information and insightsthrough a conversational interface.

FIG. 6 illustrates a method for providing a proactive alert as a naturallanguage response.

FIG. 7 depicts a transaction processing system and a process forprocessing of payment transactions.

FIG. 8 depicts a computer system architecture.

DETAILED DESCRIPTION OF THE DISCLOSED EMBODIMENTS Glossary of Terms

Payment Network—A system or network used for the transfer of money viathe use of cash-substitutes for thousands, millions, and even billionsof transactions during a given period. Payment networks may use avariety of different protocols and procedures in order to process thetransfer of money for various types of transactions. Transactions thatmay be performed via a payment network may include product or servicepurchases, credit purchases, debit transactions, fund transfers, accountwithdrawals, etc. Payment networks may be configured to performtransactions via cash-substitutes, which may include payment cards,letters of credit, checks, transaction accounts, etc. Examples ofnetworks or systems configured to perform as payment networks includethose operated by MasterCard®, VISA®, Discover®, American Express®,PayPal®, etc. Use of the term “payment network” herein may refer to boththe payment network as an entity, and the physical payment network, suchas the equipment, hardware, and software comprising the payment network.

Payment Rails—Infrastructure associated with a payment network used inthe processing of payment transactions and the communication oftransaction messages and other similar data between the payment networkand other entities interconnected with the payment network that handlesthousands, millions, and even billions of transactions during a givenperiod. The payment rails may be comprised of the hardware used toestablish the payment network and the interconnections between thepayment network and other associated entities, such as financialinstitutions, gateway processors, etc. In some instances, payment railsmay also be affected by software, such as via special programming of thecommunication hardware and devices that comprise the payment rails. Forexample, the payment rails may include specifically configured computingdevices that are specially configured for the routing of transactionmessages, which may be specially formatted data messages that areelectronically transmitted via the payment rails, as discussed in moredetail below.

Transaction Account—A financial account that may be used to fund atransaction, such as a checking account, savings account, creditaccount, virtual payment account, etc. A transaction account may beassociated with a consumer, which may be any suitable type of entityassociated with a payment account, which may include a person, family,company, corporation, governmental entity, etc. In some instances, atransaction account may be virtual, such as those accounts operated byPayPal®, etc.

Merchant—An entity that provides products (e.g., goods and/or services)for purchase by another entity, such as a consumer or another merchant.A merchant may be a consumer, a retailer, a wholesaler, a manufacturer,or any other type of entity that may provide products for purchase aswill be apparent to persons having skill in the relevant art. In someinstances, a merchant may have special knowledge in the goods and/orservices provided for purchase. In other instances, a merchant may nothave or require any special knowledge in offered products. In someembodiments, an entity involved in a single transaction may beconsidered a merchant. In some instances, as used herein, the term“merchant” may refer to an apparatus or device of a merchant entity.

Issuer—An entity that establishes (e.g., opens) a letter or line ofcredit in favor of a beneficiary, and honors drafts drawn by thebeneficiary against the amount specified in the letter or line ofcredit. In many instances, the issuer may be a bank or other financialinstitution authorized to open lines of credit. In some instances, anyentity that may extend a line of credit to a beneficiary may beconsidered an issuer. The line of credit opened by the issuer may berepresented in the form of a payment account, and may be drawn on by thebeneficiary via the use of a payment card. An issuer may also offeradditional types of payment accounts to consumers as will be apparent topersons having skill in the relevant art, such as debit accounts,prepaid accounts, electronic wallet accounts, savings accounts, checkingaccounts, etc., and may provide consumers with physical or non-physicalmeans for accessing and/or utilizing such an account, such as debitcards, prepaid cards, automated teller machine cards, electronicwallets, checks, etc.

System for Providing Sales Information and Insights through aConversational Interface

FIG. 1 depicts a system 100 for providing sales information and insightsthrough a conversational interface. The system 100 includes a processingserver 102 that considers the natural language queries 106 from themerchant 104. The merchant 104 may enter the natural language query 106into a computing device such as a smartphone, a tablet computer, alaptop computer, a personal computer, or a similar device (e.g., smarttelevision, wearable device) that is in communication with theprocessing server 102.

The natural language query 106 allows for the merchant 104 to interactwith the processing server 102 in a conversational manner. This isparticularly useful for business owners who are not familiar with thetypes of analytics that may be performed on sales information. Examplesof the conversational manner in which the natural language queries 106may be structured include “What were my sales last Friday?” and “How aremy sales this year compared to last year?” Although the queries need notbe structured in the same manner as these examples and need not have aspecific syntax or pattern, the system 100 contemplated here is able toextract the relevant intents and entities from queries 106 entered bythe merchant through natural language processing. There are a number ofways this can be done at a level of detail such as disclosed in U.S.Pat. No. 7,216,073 filed Mar. 13, 2002 and entitled “Dynamic naturallanguage understanding.”

The merchant 104 is presented with an interface through which they mayenter their natural language query 106. The merchant 104 may bepresented with a chat interface similar to one that is available throughvarious messaging systems such as Facebook Messenger®, Google Hangouts®,and WeChat®. In other embodiments, the merchant 104 may be presentedwith a customized chat interface that is provided on, for example, a webpage of the service provider that is providing sales information andinsights. For example, the merchant 104 may access a secured web siteand enter natural language queries into a text box. Particularly whenutilizing various messaging systems, the merchant 104 may appear to beinteracting with another user of the messaging system, but wouldactually be interacting with the processing server 102. Entry of thenatural language query 106 may occur through the use of a keyboard,through voice recognition, or through a combination of the keyboard andvoice recognition. Other types of input may also be used to generate thenatural language query 106. In some embodiments that utilize, forexample, voice recognition, the recognition of the voice of the merchantand the translation of the voice into a natural language query 106occurs at the computing device for the merchant 104. For example, thecomputing device for the merchant 104 may utilize a keyboard for textentry of a query and the computing device for the merchant may alsopresent a voice recognition interface for entry of a query. In eitherinstance, the computing device of the merchant 104 converts the inputinto a natural language query 106 suitable for the processing server 102by deriving at least one intent and at least one entity. An intent maybe a performance metric, a comparison metric, a timeframe, or a targetof the query. When given a natural language query 106 of “How are mysales trending this month compared to my competition,” an intent of thephrase may be “my sales” (target of query), “this month” (timeframe),and “compared to my competition” (measure of performance).

Associated with the natural language query 106 is a merchant identifierthat is associated with the merchant 104. The merchant identifier isused by the processing server 102 to retrieve the appropriateinformation from the transaction database 210 and from the paymentdatabase 206. By using the merchant identifier, the natural languageresponse 108 provided by the processing server 102 are specificallytailored to only reveal information to which the merchant 104 isentitled. For example, by associating a merchant identifier with thenatural language query 106, the merchant 104 is prevent from entering aquery such as “How were my competitor ACME Co.'s sales last week?” Thisprevents the exposure of confidential information from other merchants104. The processing server 102 may however accept certain queries wherethe natural language response 108 includes information that cannot bedirectly attributed to a specific competitor. One such example naturallanguage query 106 is “How are my sales trending this month compared tomy competition?”

Using the intent and entity derived from the natural language query 106along with the merchant identifier, the processing server 102 retrievesa plurality of transaction data entries 212 from the transactiondatabase 210 where each of the transaction data entry 212 includestransaction data associated with a payment transaction 208 from thepayment database 206 for the merchant 104. This type of information istypically found within a payment processor that handles payments betweenmerchants 104 and consumers, for example. The processing server 102 mayrequest this information from the transaction database 210 and thepayment database 206 by using the merchant identifier, for example. Thishelps ensure the generated natural language response 108 only includesthe information to which the merchant 104 is entitled. Morespecifically, when retrieving a plurality of transaction data entries212 from the transaction database 210 of a payment processor includingtransaction data associated with a payment transaction 208 from thepayment database 206 of a payment processor, information associated withanother merchant identifier will not be made available to the processingserver 102. By preventing such availability, the confidentiality ofother merchants and the confidentiality of the information of the othermerchants is ensured. Other techniques may be employed to further ensurea merchant cannot directly access the information of other merchants.The information of other merchants may, however, be provided by theprocessing server 102 in an aggregated and anonymized manner.

Using the intent and entity derived from the natural language query 106,the processing server 102 may also access additional databases 226available to the processing server 102 to expand the number of queriesthat can be responded to. external data sources 110 including paymentnetworks 112, merchant systems 114, and other data sources 116 availableto the processing server 102. Such additional databases 226 are accessedwhen the natural language query 106 provides the intent or entity wherethe external data sources 110 can provide information responsible to thequery 106.

Typically, the additional databases 226 contain information that areproprietary or otherwise not freely available for public consumption.Accordingly, in the embodiment of the processing server 102 depicted inFIG. 2, the additional databases 226 are depicted as being part of theprocessing server 102. Embodiments where the processing server 102includes the additional databases 226 may periodically receive updatesto the database 226 from a data provider or otherwise receive updates tothe information contained in the additional databases 226. In some otherembodiments, the processing server 102 securely accesses the informationfrom additional databases 226 that are not contained within theprocessing server 102. More specifically, in such an embodiment, theinformation from the additional databases 226 is received by theprocessing server 102 by way of the receiving device 202. Although theadditional databases 226 described in this example does not requirecontain confidential information, the system and method 100 describedhere also contemplates additional databases 226 which may includeinformation that is to be anonymized. Protections to ensure theconfidentiality of other merchants and other individuals and theconfidentiality of the information of the other merchants andindividuals are employed when these types of additional databases 226are utilized, but as discussed above, such information may be providedin an aggregated and anonymized manner.

Using the intent and entity derived from the natural language query 106,the processing server 102 may also access external data sources 110including payment networks 112, merchant systems 114, and other datasources 116 available to the processing server 102. Such external datasources 110 are accessed when the natural language query 106 providesthe intent or entity where the external data sources 110 can provideinformation responsible to the query 106. For example, a naturallanguage query 106 “Are my sales affected by rain?” may cause theprocessing server 102 to access other data sources 116 such as apublicly available weather database. Although the external data source110 described in this example does not require the merchant identifier,the system and method 100 described here also contemplates external datasources 110 which require the merchant identifier to provide informationto the processing server 102. When a merchant identifier is needed toaccess external data sources 110, similar protections to ensure theconfidentiality of other merchants and the confidentiality of theinformation of the other merchants, but as discussed above, suchinformation may be provided in an aggregated and anonymized manner.

By including the ability to access additional databases 226 and externaldata sources 110, the system and method 100 disclosed here increases thenumber of natural language queries 106 it is capable of providingnatural language response 106 to. For example, a natural language query106 “What is the average income for my customer base?” may cause theprocessing server 102 to access additional databases 226 or externaldata sources 110 so that sufficient information is collected to providean appropriate natural language response 108. Given the above examplenatural language query 106, the system 100 may determine the examplenatural language query 106 has an intent of determining average incomes,and an entity of the merchant 104 customer base. The processing server102 then accesses an additional database 226 containing average incomeinformation associated with street addresses, and compares thisinformation with the customer base information available to the merchant104 so that the natural language query 106 receives a response.

In another specific example of an other data source, the processingserver 102 may access a merchant server 114 to obtain information thatis not yet available in other sources such as the payment networks 112,the payment database 206, or the transaction database 210. For example,the merchant 104 may use merchant accounting software on the merchantserver 114 to store information from sales that is not transmitted topayment networks 112, the payment database 206, or the transactiondatabase 210 such as customer payment preferences and customer deliveryor pickup preferences. The processing server 102 accesses thisinformation from the merchant server 114 by using standard accesstechniques such as an application programming interface (API). Byaccessing such information, a merchant 104 can create natural languagequeries 106 that access information available only through the merchantaccounting software on the merchant system 114. For example, the naturallanguage query 106 may be “How many of my customers prefer to prepaybefore we render our services?”

The processing server 102 then identifies a set of the transaction dataentries 212 and, if needed, identifies a set of additional from externaldata sources 110 and/or additional databases 226. The processing server102 relies on the derived intent and/or the derived entity to identifythe set of the transaction data entries 212 and the set of the externalinformation from external data sources 110 and/or additional databases226. By identifying the sets, the processing server 102 reduces theamount of information that must be processed to calculate a response tothe natural language query based on the transaction data entries usingat least one analytical model. To generate the response, at least oneanalytical model is employed to process the sets of information byperforming calculations on the sets of information to derive usefulinformation.

In some embodiments, the processing server 102 may provide predictionsof future performance in response to the natural language query 106. Forinstance, the intent derived from the natural language query 106 may bea request for the prediction of future performance of the merchant 104.For example, the natural language query 106 may be “How many customersshould I expect on Friday night?” or “What will my busiest day be nextweek?” The processing server 102 may derive the intent from the naturallanguage query 106 and, based on the transaction data and other datasources 116, as discussed above, predict the future performance of themerchant 104 to answer the merchant's natural language query 106. Forexample, the processing server 102 may review the number of customers atthe merchant 104 every Friday night over time to find the average numberof Friday night customers, as well as compare the upcoming Friday nightto past Fridays (e.g., time of the month, month, specific day, weather,etc.), and predict the number of customers the merchant 104 shouldexpect for the next Friday. The processing server 102 may then use theprediction in the response being provided to the merchant 104.

To provide the response to the merchant 104 in a conversational manner,a natural language response 108 is generated from the response by theprocessing server 102. The natural language response 108 is thenprovided to the merchant 104 in a manner similar to how the naturallanguage query 106 was received. More specifically, the interfacethrough which the merchant 104 entered the natural language query 106 isutilized to provide the natural language response 108. In someembodiments, the natural language query 106 may be provided to themerchant 104 in a periodic manner or in a delayed manner so the merchant104 approves the use of an alternate interface for the system 100 toprovide the natural language response 108. Other types of naturallanguage queries 106 may be provided such as a natural language query106 with a scheduled task having a triggering threshold. Such a naturallanguage query 106 may be “Send me a message if today's sales are 20%lower than historical values.” With such a natural language query 106,the processing server 102 monitors the relevant database and/or datasource so that when the triggering threshold is met, a natural language108 is provided. For example, a merchant 104 may submit a naturallanguage query 106 by interacting with a Google Hangouts bot whichrequires consideration of a substantial amount of information.Accordingly, the merchant 104 may approve receipt of the naturallanguage response 108 by way of an SMS message. As another example, amerchant 104 may submit a natural language query 106 using GoogleHangouts by asking “Send me a text message if today's sales are 20%lower than historical values.” Regardless of the interface used, thenatural language response 108 is transmitted so that the merchant 104may consider the information contained in the natural language response108. The natural language response 108 is received by the computingdevice being used by the merchant 104. In some embodiments, the naturallanguage response 108 is received by an alternate device such as a voicenatural language response by way of a voice telephone call.

In some embodiments, the processing server 102 may be configured toprovide natural language responses 108 as proactive alerts to themerchant 104, such as in instances where no natural language query 106may be submitted. For instance, the processing server 102 may utilizethe transaction data and other data sources 116 to identify metricsbased on the current status of the merchant 104, such as with respect toboth the market performance of the merchant 104 and demographics of theconsumer base of the merchant 104. The processing server 102 may comparethese metrics with prior performance of the merchant 104, one or morerelated merchants, the market industry of the merchant 104, a geographicarea that includes the merchant 104, etc. If the current metrics of themerchant 104 are different from the compared metrics by a predeterminedthreshold (e.g., one or more standard deviations, a percentage, etc.),then the processing server 102 may provide the merchant 104 with aproactive alert. The proactive alert may be a natural language response108 that is generated by the processing server 102 that includes themerchant's current status and the difference between the merchant'scurrent status and the compared metric(s).

For example, the merchant 104 may continue doing business and may notsubmit a natural language query 106. The processing server 102 maymonitor the current performance of the merchant 104 and may identify aninstance when the merchant's performance deviates from their historicalperformance. In such an instance, the processing server 102 may generatea natural language response 108 to provide to the merchant 104. Forinstance, the processing server 102 may identify a change in one of themarket demographics for the merchant 104 and provide a natural languageresponse 108 stating that “It looks like 3 months ago, 30% of yourcustomers were females between the ages of 25 and 35. This month theymade up less than 5% of your business. You should target them withadvertising to get them back.” In some cases, the processing server 102may also provide proactive alerts when merchant performance is betterthan past or comparable metrics. For example, the natural languageresponse 108 for a proactive alert may state “Your sales were up over10% last week. That's about 50% better than your competition.”

The natural language response 108 considered by the merchant 104 mayinclude audio, graphical, textual, or other types of information thesystem 100 considers useful for understanding the calculated response.For example, the calculated response may result be a sales increase peryear derived from transaction data entries. With such a calculatedresponse, the natural language response 108 may include a graphicalrepresentation of the annual sales increase derived from transactiondata entries so that consideration by the merchant 104 may befacilitated.

In at least some situations, the merchant 104 may desire to understandthe reasons the natural language response 108 was provided in responseto the natural language query 106. When the merchant 104 provides anatural language query 106 that desires the reasons the response 108 wasgenerated, the system 100 provides the reasoning contained in theanalytical models used to calculate the response on which the naturalresponse is based 108.

In some embodiments, the processing server 102 may utilize an artificialintelligence (AI). The processing server 102 may use an AI to identifymetrics for inclusion in a natural language response 108, including forthe identification of predictions for future performance based onhistorical data for the merchant 104, related merchants, the marketindustry of the merchant 104, a geographic area including the merchant104, etc. Additional data regarding the use of AI for predictive modelsand identifying predictions of future performance can be found in U.S.patent application Ser. No. 14/941,586, entitled “Method of OperatingArtificial Intelligence Machines to Improve Predictive Model Trainingand Performance,” filed on Nov. 14, 2015, which is herein incorporatedby reference in its entirety. The AI may be configured to identifypredictions, as well as identify proactive alerts for inclusion innatural language responses 108. In some instances, the AI may improveintelligence based on merchant feedback and other data. For example, themerchant 104 may provide feedback on the effectiveness of proactivealerts, which the AI may use to improve alerts that are provided to themerchant 104. In another example, the AI may analyze transaction dataand other data received that is related to a prior prediction providedto the merchant 104, such as to determine the effectiveness of such aprediction for use in improving future predictions. For instance, the AImay provide a prediction of the number of customers the merchant 104will have on a given night, may analyze the actual number of customersthe merchant 104 had on the night, and may use such data (e.g., howclose the prediction was, if it was above or below the actual amount,etc.) and use that data to improve the AI for future predictions.

Processing Server

FIG. 2 illustrates an embodiment of a processing server 102 in thesystem 100. It will be apparent to persons having skill in the relevantart that the embodiment of the processing server 102 illustrated in FIG.2 is provided as illustration only and may not be exhaustive to allpossible configurations of the processing server 102 suitable forperforming the functions as discussed herein. For example, the computersystem 700 illustrated in FIG. 7 and discussed in more detail below maybe a suitable configuration of the processing server 102.

The processing server 102 may include a receiving device 202. Thereceiving device 202 may be configured to receive data over one or morenetworks via one or more network protocols. In some embodiments, thereceiving device 202 may be configured to receive data over the paymentrails, such as using specially configured infrastructure associated withpayment networks 116 for the transmission of transaction messages thatinclude sensitive financial data and information. In some instances, thereceiving device 202 may also be configured to receive data fromticketing entities 106, computing devices 110, payment networks 116, andother entities via alternative networks, such as the Internet. In someembodiments, the receiving device 202 may be comprised of multipledevices, such as different receiving devices for receiving data overdifferent networks, such as a first receiving device for receiving dataover payment rails and a second receiving device for receiving data overthe Internet. The receiving device 202 may receive electronicallytransmitted data signals, where data may be superimposed or otherwiseencoded on the data signal and decoded, parsed, read, or otherwiseobtained via receipt of the data signal by the receiving device 202. Insome instances, the receiving device 202 may include a parsing modulefor parsing the received data signal to obtain the data superimposedthereon. For example, the receiving device 202 may include a parserprogram configured to receive and transform the received data signalinto usable input for the functions performed by the processing deviceto carry out the methods and systems described herein.

The receiving device 202 may be configured to receive data signalselectronically transmitted by computing devices employed by merchants104, which may be superimposed or otherwise encoded with data. Forexample, natural language queries 106 will be received by the receivingdevice 202. The natural language queries 106 may include, for example,the textual equivalent of the actual query presented by the merchant104, a preprocessed equivalent of the actual query presented by themerchant 104, or a combination thereof. In some embodiments, the naturallanguage query 106 may include data indicating at least one intent andat least one entity. In other embodiments, the natural language query106 may include data that needs additional processing to derive the atleast one intent and at least one entity. The receiving device 202 mayalso be configured to receive data signals electronically transmitted bydata providers for the information contained in additional databases226, external data sources 110 such as payment networks 112, merchantsystems 114, and other publicly available data sources such ashistorical weather reports, geographical information, and otherinformation suitable for further processing. In some embodiments, thereceiving device 202 may be further configured to receive data forstorage in the processing server 102, as discussed in more detail below,such as the received natural language query 106, previously retrieveddata from external data sources 110 that need not be refreshed, andother information received by the processing server 102.

The processing server 102 may also include a communication module 204.The communication module 204 may be configured to transmit data betweenmodules, engines, databases, memories, and other components of theprocessing server 102 for use in performing the functions discussedherein. The communication module 204 may be comprised of one or morecommunication types and utilize various communication methods forcommunications within a computing device. For example, the communicationmodule 204 may be comprised of a bus, contact pin connectors, wires,etc. In some embodiments, the communication module 204 may also beconfigured to communicate between internal components of the processingserver 102 and external components of the processing server 102, such asexternally connected databases, display devices, input devices, etc. Theprocessing server 102 may also include a processing device. Theprocessing device may be configured to perform the functions of theprocessing server 102 discussed herein as will be apparent to personshaving skill in the relevant art. In some embodiments, the processingdevice may include and/or be comprised of a plurality of engines and/ormodules specially configured to perform one or more functions of theprocessing device, such as a querying module 214, calculation module216, response generation module 218, natural language processor 224, andnatural language generator 226. As used herein, the term “module” may besoftware or hardware particularly programmed to receive an input,perform one or more processes using the input, and provides an output.The input, output, and processes performed by various modules will beapparent to one skilled in the art based upon the present disclosure.

The processing server 102 includes a payment database 206 that containspayment transactions 208 and a transaction database 210 that containstransaction data entries 212. Such payment transactions 208 andtransaction data entries 212 are associated with at least one merchantidentifier so that only the payment transactions 208 and the transactiondata entries 212 the merchant 104 is entitled to are provided. Suchenforcement of confidentiality may occur at the payment transaction 208,the payment database 206, the transaction data entries 212, thetransaction database 210, or the querying module 214. In someembodiments, enforcement of confidentiality occurs at multiple levels toprovide reliable confidentiality.

The processing server 102 may include a querying module 214. Thequerying module 214 may be configured to execute queries on databases206, 210 to identify sets of information. More specifically, thequerying module 214 may retrieve a plurality of data entries andidentify a set of the plurality of entries that satisfy certain criteriato reduce the amount of data that must be considered when calculating aresponse. The querying module 214 may receive one or more data values orquery strings, and may execute a query string based thereon on anindicated database 206, 210 to identify information stored therein. Thequerying module 214 may then output the identified sets of informationto an appropriate engine or module of the processing server 102 asnecessary. The querying module 214 may, for example, execute a query onthe payment database 206 to identify payment transactions 208 related toa merchant 104 with a merchant identifier. The querying module 214 mayalso execute queries on the transaction database 210, such as toidentify transaction data entries 212 related to a merchant 104 with amerchant identifier. In some embodiments, the querying module 214 maymonitor database 206, 210 for specific trends or transactions. Forexample, the querying module 214 may monitor database 206, 210 todetermine if the day's sales are lower than historical sales. Thequerying module 214 may also utilize the at least one extent and entityobtained from the natural language query 106 to execute a query.

The querying module 214 may also execute queries on external datasources 110 and additional databases 226. The querying module 214 mayemploy any appropriate API to obtain the desired information from theexternal data sources 110 and additional databases 226. Morespecifically, the querying module 214 may retrieve a plurality of dataentries and identify a set of the plurality of entries that satisfycertain criteria to reduce the amount of data that must be consideredwhen calculating a response. Certain external data sources 110 andadditional databases 226 may contain information that may revealinformation to merchant 104 that it is not entitled to. For example, apayment network 112 may provide access to payment transactions based ona geographic location. Information obtained from the payment network 112that includes merchant identifiers will be modified by the queryingmodule 214 to ensure the merchant 104 only obtains information for whichit is entitled.

The processing server 102 may also include a calculation module 216. Thecalculation module 216 may be configured to perform determinations forthe processing server 102 for performing the functions discussed herein.The calculation module 216 may receive instructions as input, mayperform determinations based on the instructions, and output a result ofthe calculation in accordance with an analytical model to another moduleor engine of the processing server 102. In some instances, thecalculation module 216 may receive data for use in the calculation asinput. In other instances, the calculation module 216 may be configuredto obtain data for use in calculations, such as by instructing thequerying module 214 to perform corresponding queries. The calculationmodule 216 may be configured to calculate, in accordance with ananalytical model, useful information responsive to the natural languagequery 106. Multiple analytical models performing in an ensemble may alsobe used by the calculation module 216 to perform calculations responsiveto the natural language query 106. In some embodiments, the calculationmodule 216 may execute calculations to identify specific trends ortransactions. For example, the calculation module 216 may executecalculations to determine if the day's sales are lower than historicalsales. In some embodiments, the calculation module 216 may be furtherconfigured to perform calculations irrespective of a natural languagequery 106, such as for the identification of metrics for the merchant104 or other merchants, market industries, market demographics, etc.,such as may be used for proactive alerts.

The processing server 102 also includes a natural language responsemodule 218. The natural language response module 218 generates a naturallanguage response 108 based on the result of the calculation by thecalculation module 216. The natural language response 108 is aconversational response to the natural language query 106 by themerchant 104. In some embodiments, based on the natural language query106 by the merchant 104, the queries made by the querying module 214,and the calculations made by the calculating module 216, the naturallanguage response module 218 generates a natural language response 108to be provided to the merchant 104.

The natural language response 108 generated by the natural languageresponse module 218 may include audio, graphical, textual, or othertypes of information considered useful for facilitating theunderstanding of the merchant 104 of the calculated response. Forexample, the calculated response may result be a sales increase per yearderived from transaction data entries. With such a calculated response,the natural language response 108 generated by the natural languageresponse module 218 may include a graphical representation of the annualsales increase derived from transaction data entries so thatconsideration by the merchant 104 may be facilitated.

When the natural language query 106 from the merchant 104 seeks thereasons why a natural language response 108 was provided, the processingserver 102 provides the merchant 104 with the analytical models used bythe calculation module 216 so that the merchant 104 can have an improvedunderstanding as to why the natural language response 108 was provided.

The processing server 102 may also include a predictive module 228. Thepredictive module 228 may be configured to make predictions for theprocessing server 102 as part of the functions of the processing server102 discussed herein. The predictive module 228 may receive aninstruction as input, may make a prediction based on the instruction,and may output the prediction to another module or engine of theprocessing server 102. In some instances, the instruction may beaccompanied by data for use by the predictive module 228 in identifyingthe prediction. In other instances, the predictive module 228 may beconfigured to identify (e.g., via instructions to the querying module214) data for use in the requested prediction. The predictive module 228may use one or more predictive models in making predictions, which maybe generated based on data trends as analyzed by the modules and enginesof the processing server 102. In some embodiments, the predictive module228 may utilize an AI, such as may be stored in a memory 222 of theprocessing server 102, for identifying predictions. In some cases, thepredictive module 228 may update predictive models based on results ofpredictions made thereby, such as by comparing predictions to datareceived by the receiving device 202 corresponding to the target of thepredictions.

The processing server 102 may also include a transmitting device 220.The transmitting device 220 may be configured to transmit data over oneor more networks via one or more network protocols. In some embodiments,the transmitting device 220 may be configured to transmit data over thepayment rails, such as using specially configured infrastructureassociated with payment networks 116 for the transmission of transactionmessages that include sensitive financial data and information, such asidentified payment credentials. In some instances, the transmittingdevice 220 may be configured to transmit data to computing devices beingused by merchants 104 and external data sources 110 such as paymentnetworks 112, merchant systems 114, and other data sources 116. In someembodiments, the transmitting device 220 may be comprised of multipledevices, such as different transmitting devices for transmitting dataover different networks, such as a first transmitting device fortransmitting data over the payment rails and a second transmittingdevice for transmitting data over the Internet. The transmitting device220 may electronically transmit data signals that have data superimposedthat may be parsed by a receiving computing device. In some instances,the transmitting device 220 may include one or more modules forsuperimposing, encoding, or otherwise formatting data into data signalssuitable for transmission.

The processing server 102 may also include a memory 222. The memory 222may be configured to store data for use by the processing server 102 inperforming the functions discussed herein. The memory 222 may beconfigured to store data using suitable data formatting methods andschema and may be any suitable type of memory, such as read-only memory,random access memory, etc. The memory 222 may include, for example,encryption keys and algorithms, communication protocols and standards,data formatting standards and protocols, program code for modules andapplication programs of the processing device, and other data that maybe suitable for use by the processing server 102 in the performance ofthe functions disclosed herein as will be apparent to persons havingskill in the relevant art. In some embodiments, the memory 222 may becomprised of or may otherwise include a relational database thatutilizes structured query language for the storage, identification,modifying, updating, accessing, etc. of structured data sets storedtherein.

The memory 222 may be configured to store data associated with an AI andpredictive models for use thereof. The memory 222 may also be configuredto store metrics, wherein the metrics may be related to merchantperformance and/or market demographics. The metrics may be associatedwith a time and/or date, geographic location, the merchant 104, one ormore related merchants, the merchant industry involving the merchant104, etc. For example, the metrics may include market performance of themerchant industry involving the merchant 104 in a geographic areaincluding the merchant 104, or any other metric that may be useful for amerchant 104 as will be apparent to persons having skill in the relevantart.

A natural language processor 224 is utilized by the processing server102 to process the query 106 and derive at least one intent and at leastone entity from the query 106. The natural language query 106 isreceived by the processing server 102 through the receiving device 202.The natural language query 106 is received either directly from themerchant 104 or from a computing device for the merchant 104 such as asmartphone. In some embodiments, the natural language processor 224 maybe a computer or a network of computers dedicated to natural languageprocessing. In such embodiments, the computer or network of computersdedicated to natural language processing communicate with the processingserver 102 to serve the function of the natural language processor 224depicted in FIG. 2. The natural language processor 224 uses generallearning algorithms that may be grounded in statistical inferences toautomatically learn language rules through the analysis of a corpus ofprior interactions. Multiple classes of machine learning algorithms maybe employed including decision trees and probabilistic decision modelsusing real-valued weights so that a relative certainty as to theintended meaning of the natural language query 106 may be derived. Insome embodiments, the natural language processor 224 uses feedback fromthe merchant 104 to improve its processing of future natural languagequeries 106. In still further embodiments, a human will review theinteractions between the merchant 104 and the system 100 and provideinput to improve the processing of future natural language queries 106by the natural language processor 224. In some embodiments, the generallearning algorithms are combined with hard coded sets of rules tofurther improve processing of the query 106.

Regardless of the specific algorithm or combination of algorithmsemployed, the natural language processor 224 extracts at least oneintent and at least one entity from the natural language query 106. Thenatural language processor 224 provides such intent and entityinformation to, for example, the querying module 214, calculation module216, and the natural language response module 218. Such informationprovided by the natural language processor 224 provides the informationneeded for the other modules 214, 216, 218 need to perform theirfunctions.

Method for Providing Sales Information and Insights through aConversational Interface

FIG. 3 depicts a method 300 for providing sales information and insightsthrough a conversational interface.

At step 302, the merchant 104 creates a natural language query 106 byproviding input to an interface presented by a computing device. Theprocessing server 102 receives the natural language query 106 from thecomputing device being used by the merchant 104 at step 306. Here, thereceiving device 202 of a processing server 102 may be employed toreceive the natural language query 106 from the computing device beingused by the merchant 104.

The natural language query 106 is processed by the natural I languageprocessor 224 to derive the intent and entity at step 308. Generallearning algorithms grounded in statistical inferences may be employedto derive the intent and entity of natural language queries 106.Multiple classes of machine learning algorithms may be employedincluding decision trees and probabilistic decision models usingreal-valued weights so that a relative certainty as to the intendedmeaning of the natural language query 106 may be derived.

Step 310 of the method retrieves data entries from data sources usingthe querying module 214 of the processing server 102 where the datasources include a payment database 206, a transaction database 210,additional databases 226, and external data sources 110. Morespecifically, step 310 retrieves a plurality of data entries andidentifies a set of the plurality of entries that satisfy certaincriteria. During the execution of step 310, a query on the paymentdatabase 206 to identify payment transactions 208 related to a merchant104 with a merchant identifier may execute either sequentially or inparallel with other queries such as queries on the transaction database210 to identify transaction data entries 212 related to a merchant 104with a merchant identifier. In addition, a query to an additionaldatabase 226 or an external data source 110 may execute eithersequentially or in parallel with other queries so that data entries areprovided as shown in step 320. The external data sources 110 may includemerchant account software on the merchant server 114, for example. Step312 of the method uses the querying module 214 of the processing server102 to identify a set of data entries from a payment database 206, atransaction database 210, additional databases 226, and external datasources 110 that correspond to the derived intent and entity.

Step 314 of the method calculates a response using the calculationmodule 216 based on the set of data entries that correspond to thederived intent and entity. The calculated response may be made based onat least one analytical model. Multiple analytical models performing inan ensemble may also be used by the calculation module 216 to performcalculations responsive to the natural language query 106. In someembodiments, the calculation module 216 may execute calculations toidentify specific trends or transactions. For example, the calculationmodule 216 may execute calculations to determine if the day's sales arelower than historical sales. In some instances, the natural languagequery 106 may request a prediction. In such instances, the calculationmodule 216 may utilize the predictive module 228, which may utilize onor more prediction models to identify the prediction requested by themerchant 104 based on the set of data entries that correspond to thederived intent and entity.

Step 316 of the method generates a natural language response 108 basedon the calculated response. The natural language response 108 mayinclude audio, graphical, textual, or other types of information thesystem 100 considers useful for understanding the calculated response.For example, the calculated response may result be a discrete salesincrease per year derived from transaction data entries (e.g., 5% salesincrease in year 1, 3% sales increase in year 2, etc.). With such acalculated response, the natural language response 108 generated by thenatural language response module 218 may include a graphicalrepresentation of the annual sales increase derived from transactiondata entries so that consideration by the merchant 104 may befacilitated. When the natural language query 106 from the merchant 104seeks the reasons why a natural language response 108 was provided, themerchant 104 is provided with the analytical models used so that themerchant 104 can have an improved understanding as to why the naturallanguage response 108 was provided.

In step 318, the natural language response 108 is transmitted to thecomputing device being used by the merchant 104 which is then receivedby the computing device at step 304. The transmitting device 220 of theprocessing server 102 is employed in order to transmit the naturallanguage response 108. In instances where the natural language response108 included a prediction, the process may also include the receipt offeedback by the receiving device 202 of the processing server 102 fromthe merchant 104 or in received transaction data, which may be used bythe predictive module 228 in updating the predictive model(s) used inmaking the prediction based on corresponding data.

Process for Providing Sales Information and Insights through aConversational Interface

FIG. 4 depicts the process 400 of providing sales information andinsights through a conversational interface.

At step 402, the processing server 102 receives the natural languagequery 106 from the computing device being used by the merchant 104.Here, the receiving device 202 of a processing server 102 may beemployed to receive the natural language query 106 from the computingdevice being used by the merchant 104.

At step 404, the processing server 102 processes the natural languagequery using the natural language processor 224. The natural languagequery 106 is processed to derive the intent and entity. General learningalgorithms grounded in statistical inferences may be employed to derivethe intent and entity of natural language queries 106. Multiple classesof machine learning algorithms may be employed including decision treesand probabilistic decision models using real-valued weights so that arelative certainty as to the intended meaning of the natural languagequery 106 may be derived.

At steps 406 and 408, the processing server 102 uses a querying module214 to retrieves data entries from data sources including a paymentdatabase 206, a transaction database 210, additional databases 226, andexternal data sources 110. More specifically, step 406 retrieves aplurality of data entries and identifies a set of the plurality ofentries that satisfy certain criteria. During the execution of step 406,a query on the payment database 206 to identify payment transactions 208related to a merchant 104 with a merchant identifier may execute eithersequentially or in parallel with other queries such as queries on thetransaction database 210 to identify transaction data entries 212related to a merchant 104 with a merchant identifier. In addition, aquery to an external data source 110 may execute either sequentially orin parallel with other queries so that data entries are provided. Theexternal data sources 110 may include merchant account software on themerchant server 114, for example. Step 408 of the method identifies aset of data entries from a payment database 206, a transaction database210, an additional database 226, and external data sources 110 thatcorrespond to the derived intent and entity.

A response is calculated at step 410 using the calculation module 216 ofthe processing server 102 based on the set of data entries thatcorrespond to the derived intent and entity. The calculated response maybe made based on at least one analytical model. Multiple analyticalmodels performing in an ensemble may also be used by the calculationmodule 216 to perform calculations responsive to the natural languagequery 106. In some embodiments, the calculation module 216 may executecalculations to identify specific trends or transactions. For example,the calculation module 216 may execute calculations to determine if theday's sales are lower than historical sales. In instances where aprediction is requested, the calculation module 216 may request thepredictive module 228 to provide a prediction based on the derivedintent and entity using one or more predictive models, which, in somecases, may utilize an AI.

A natural language response 108 is generated at step 412 using thenatural language response module 218 of the processing server 102. Thenatural language response 108 is based on the calculated responsegenerated at step 410. The natural language response 108 may includeaudio, graphical, textual, or other types of information the system 100considers useful for understanding the calculated response. For example,the calculated response of step 410 may be a discrete sales increase peryear derived from transaction data entries (e.g., 5% sales increase inyear 1, 3% sales increase in year 2, etc.). With such a calculatedresponse, the natural language response 108 generated by the naturallanguage response module 218 may include a graphical representation ofthe annual sales increase derived from transaction data entries so thatconsideration by the merchant 104 may be facilitated. When the naturallanguage query 106 from the merchant 104 seeks the reasons why a naturallanguage response 108 was provided, the merchant 104 is provided withthe analytical models used so that the merchant 104 can have an improvedunderstanding as to why the natural language response 108 was provided.

The natural language response 108 generated at step 412 is thentransmitted to the computing device being used by the merchant 104 atstep 414. The transmitting device 220 of the processing server 102 isemployed in order to transmit the natural language response 108.

Exemplary Method for Providing Sales Information and Insights through aConversational Interface.

FIG. 5 illustrates a method 500 for providing sales information andinsights through a conversational interface.

At step 502, a natural language query 106 is received from a computingdevice being used by a merchant 104. The merchant 104 is associated witha merchant identifier to ensure the merchant 104 only receivesinformation to which it is entitled. The computing device being used bythe merchant 104 provides an interface through which the naturallanguage query 106 is entered.

At step 504, the natural language query 106 is processed by the naturallanguage processor 224 of the processing server 102. The naturallanguage processor 224 derives at least one intent and at least oneentity from the natural language query. An intent may be a performancemetric, a comparison metric, a timeframe, or a target of the query. Whengiven a natural language query 106 of “How are my sales trending thismonth compared to my competition,” an intent of the phrase may be “mysales” (target of query), “this month” (timeframe), and “compared to mycompetition” (measure of performance).

At steps 506 and 508, transaction data entries are retrieved (step 506)by the querying module 214 of the processing server 102, and sets of thetransaction data entries that correspond to the at least one intent orthe at least one entity are identified (step 508). More particularly,step 506 involves the querying module 214 executing queries on databases206, 210 to retrieve payment transactions 208 and transaction dataentries 212 relevant to responding to the natural language query 106. Atstep 508, the querying module 214 identifies a set of the data thatsatisfy certain criteria to reduce the amount of data that must beconsidered when calculating a response.

At step 510, a response to the natural language query 106 is calculatedby the calculation module 216. The response is based on the set of thetransaction data entries retrieved by the querying module 214. Theresponse is also based on an analytical model used by the calculationmodule 216 to process the retrieved data. When the merchant 104 requeststhe reasoning behind a particular natural language response 108, theanalytical models used for the response are provided to the naturallanguage response module 218 for further processing.

At step 512, a natural language response 108 to the natural languagequery 106 is generated by the natural language response module 218 basedon the calculated response. The natural language response 108 is aconversational response to the natural language query 106 by themerchant 104. In some embodiments, based on the natural language query106 by the merchant 104, the queries made by the querying module 214,and the calculations made by the calculating module 216, the naturallanguage response module 218 generates a natural language response 108to be provided to the merchant 104. The natural language response 108generated by the natural language response module 218 may include audio,graphical, textual, or other types of information considered useful forfacilitating the understanding of the merchant 104 of the calculatedresponse. For example, the calculated response may result be a salesincrease per year derived from transaction data entries. With such acalculated response, the natural language response 108 generated by thenatural language response module 218 may include a graphicalrepresentation of the annual sales increase derived from transactiondata entries so that consideration by the merchant 104 may befacilitated. When the merchant 104 requests the reasoning behind aparticular natural language response 108, the analytical models areprocessed by the natural language response module 218 to facilitateconsumption by the merchant 104.

At step 514, the generated natural language response 108 is transmittedto the computing device being used by the merchant 104. The computingdevice being used by the merchant 104 receives the generated naturallanguage response 108 and presents the generated natural languageresponse 108 using the same interface through which the natural languagequery 106 was received. In some embodiments, the merchant 104 may preferto receive the generated natural language response 108 through anotherinterface.

Exemplary Method for Providing a Proactive Alert as a Natural LanguageMessage

FIG. 6 illustrates a method 600 for the providing of a proactive alertto a merchant regarding merchant metrics using a natural languagemessage.

In step 602, a plurality of transaction data entries (e.g., transactiondata entries 212) may be stored in a transaction database (e.g., thetransaction database 210) of a processing server (e.g., the processingserver 102), wherein each transaction data entry is related to a paymenttransaction involving a merchant (e.g., the merchant 104) and includestransaction data. In step 604, a plurality of metrics may be stored in amemory (e.g., the memory 222) of the processing server, wherein each ofthe plurality of metrics is related to at least one of: merchantperformance and market demographics for at least one of: the merchant, ageographic area including the merchant, a related merchant, and a marketindustry related to the merchant.

In step 606, a present metric for the merchant may be identified by acalculation module (e.g., the calculation module 216) of the processingserver based on at least the transaction data included in one or moretransaction data entries stored in the transaction database, wherein thepresent metric is related to merchant performance or market demographicsfor the merchant and is different from one of the plurality of metricsstored in the memory by at least predetermined threshold. In step 608, anatural language response (e.g., the natural language response 108) maybe generated by a natural language response module (e.g., the naturallanguage response module 218) of the processing server, wherein thenatural language response includes at least the identified presentmetric and a difference between the identified present metric and theone of the plurality of metrics. In step 610, the generated naturallanguage response may be electronically transmitted by a transmittingdevice (e.g., the transmitting device 220) of the processing server to acomputing system associated with the merchant.

Payment Transaction Processing System and Process

FIG. 7 illustrates a transaction processing system and a process 700 forthe processing of payment transactions in the system, which may includethe processing of thousands, millions, or even billions of transactionsduring a given period (e.g., hourly, daily, weekly, etc.). The process700 and steps included therein may be performed by one or morecomponents of the system 100 discussed above, such as the processingserver 102, merchant 104, etc. The processing of payment transactionsusing the system and process 700 illustrated in FIG. 7 and discussedbelow may utilize the payment rails, which may be comprised of thecomputing devices and infrastructure utilized to perform the steps ofthe process 700 as specially configured and programmed by the entitiesdiscussed below, including the transaction processing server 712, whichmay be associated with one or more payment networks configured toprocessing payment transactions. It will be apparent to persons havingskill in the relevant art that the process 700 may be incorporated intothe processes illustrated in FIGS. 3-6, discussed above, with respect tothe step or steps involved in the processing of a payment transaction.In addition, the entities discussed herein for performing the process700 may include one or more computing devices or systems configured toperform the functions discussed below. For instance, the merchant 706may be comprised of one or more point of sale devices, a localcommunication network, a computing server, and other devices configuredto perform the functions discussed below.

In step 720, an issuing financial institution 702 may issue a paymentcard or other suitable payment instrument to a consumer 704. The issuingfinancial institution may be a financial institution, such as a bank, orother suitable type of entity that administers and manages paymentaccounts and/or payment instruments for use with payment accounts thatcan be used to fund payment transactions. The consumer 704 may have atransaction account with the issuing financial institution 702 for whichthe issued payment card is associated, such that, when used in a paymenttransaction, the payment transaction is funded by the associatedtransaction account. In some embodiments, the payment card may be issuedto the consumer 704 physically. In other embodiments, the payment cardmay be a virtual payment card or otherwise provisioned to the consumer704 in an electronic format.

In step 722, the consumer 704 may present the issued payment card to amerchant 706 for use in funding a payment transaction. The merchant 706may be a business, another consumer, or any entity that may engage in apayment transaction with the consumer 704. The payment card may bepresented by the consumer 704 via providing the physical card to themerchant 706, electronically transmitting (e.g., via near fieldcommunication, wireless transmission, or other suitable electronictransmission type and protocol) payment details for the payment card, orinitiating transmission of payment details to the merchant 706 via athird party. The merchant 706 may receive the payment details (e.g., viathe electronic transmission, via reading them from a physical paymentcard, etc.), which may include at least a transaction account numberassociated with the payment card and/or associated transaction account.In some instances, the payment details may include one or moreapplication cryptograms, which may be used in the processing of thepayment transaction.

In step 724, the merchant 706 may enter transaction details into a pointof sale computing system. The transaction details may include thepayment details provided by the consumer 704 associated with the paymentcard and additional details associated with the transaction, such as atransaction amount, time and/or date, product data, offer data, loyaltydata, reward data, merchant data, consumer data, point of sale data,etc. Transaction details may be entered into the point of sale system ofthe merchant 706 via one or more input devices, such as an optical barcode scanner configured to scan product bar codes, a keyboard configuredto receive product codes input by a user, etc. The merchant point ofsale system may be a specifically configured computing device and/orspecial purpose computing device intended for the purpose of processingelectronic financial transactions and communicating with a paymentnetwork (e.g., via the payment rails). The merchant point of sale systemmay be an electronic device upon which a point of sale systemapplication is run, wherein the application causes the electronic deviceto receive and communicated electronic financial transaction informationto a payment network. In some embodiments, the merchant 706 may be anonline retailer in an e-commerce transaction. In such embodiments, thetransaction details may be entered in a shopping cart or otherrepository for storing transaction data in an electronic transaction aswill be apparent to persons having skill in the relevant art.

In step 726, the merchant 706 may electronically transmit a data signalsuperimposed with transaction data to a gateway processor 708. Thegateway processor 708 may be an entity configured to receive transactiondetails from a merchant 706 for formatting and transmission to anacquiring financial institution 710. In some instances, a gatewayprocessor 708 may be associated with a plurality of merchants 706 and aplurality of acquiring financial institutions 710. In such instances,the gateway processor 708 may receive transaction details for aplurality of different transactions involving various merchants, whichmay be forwarded on to appropriate acquiring financial institutions 710.By having relationships with multiple acquiring financial institutions710 and having the requisite infrastructure to communicate withfinancial institutions using the payment rails, such as usingapplication programming interfaces associated with the gateway processor708 or financial institutions used for the submission, receipt, andretrieval of data, a gateway processor 708 may act as an intermediaryfor a merchant 706 to be able to conduct payment transactions via asingle communication channel and format with the gateway processor 708,without having to maintain relationships with multiple acquiringfinancial institutions 710 and payment processors and the hardwareassociated thereto. Acquiring financial institutions 710 may befinancial institutions, such as banks, or other entities thatadministers and manages payment accounts and/or payment instruments foruse with payment accounts. In some instances, acquiring financialinstitutions 710 may manage transaction accounts for merchants 706. Insome cases, a single financial institution may operate as both anissuing financial institution 702 and an acquiring financial institution710.

The data signal transmitted from the merchant 706 to the gatewayprocessor 708 may be superimposed with the transaction details for thepayment transaction, which may be formatted based on one or morestandards. In some embodiments, the standards may be set forth by thegateway processor 708, which may use a unique, proprietary format forthe transmission of transaction data to/from the gateway processor 708.In other embodiments, a public standard may be used, such as theInternational Organization for Standardization's ISO 8783 standard. Thestandard may indicate the types of data that may be included, theformatting of the data, how the data is to be stored and transmitted,and other criteria for the transmission of the transaction data to thegateway processor 708.

In step 728, the gateway processor 708 may parse the transaction datasignal to obtain the transaction data superimposed thereon and mayformat the transaction data as necessary. The formatting of thetransaction data may be performed by the gateway processor 708 based onthe proprietary standards of the gateway processor 708 or an acquiringfinancial institution 710 associated with the payment transaction. Theproprietary standards may specify the type of data included in thetransaction data and the format for storage and transmission of thedata. The acquiring financial institution 710 may be identified by thegateway processor 708 using the transaction data, such as by parsing thetransaction data (e.g., deconstructing into data elements) to obtain anaccount identifier included therein associated with the acquiringfinancial institution 710. In some instances, the gateway processor 708may then format the transaction data based on the identified acquiringfinancial institution 710, such as to comply with standards offormatting specified by the acquiring financial institution 710. In someembodiments, the identified acquiring financial institution 710 may beassociated with the merchant 706 involved in the payment transaction,and, in some cases, may manage a transaction account associated with themerchant 706.

In step 730, the gateway processor 708 may electronically transmit adata signal superimposed with the formatted transaction data to theidentified acquiring financial institution 710. The acquiring financialinstitution 710 may receive the data signal and parse the signal toobtain the formatted transaction data superimposed thereon. In step 732,the acquiring financial institution may generate an authorizationrequest for the payment transaction based on the formatted transactiondata. The authorization request may be a specially formatted transactionmessage that is formatted pursuant to one or more standards, such as theISO 8783 standard and standards set forth by a payment processor used toprocess the payment transaction, such as a payment network. Theauthorization request may be a transaction message that includes amessage type indicator indicative of an authorization request, which mayindicate that the merchant 706 involved in the payment transaction isrequesting payment or a promise of payment from the issuing financialinstitution 702 for the transaction. The authorization request mayinclude a plurality of data elements, each data element being configuredto store data as set forth in the associated standards, such as forstoring an account number, application cryptogram, transaction amount,issuing financial institution 702 information, etc.

In step 734, the acquiring financial institution 710 may electronicallytransmit the authorization request to a transaction processing server712 for processing. The transaction processing server 712 may becomprised of one or more computing devices as part of a payment networkconfigured to process payment transactions. In some embodiments, theauthorization request may be transmitted by a transaction processor atthe acquiring financial institution 710 or other entity associated withthe acquiring financial institution. The transaction processor may beone or more computing devices that include a plurality of communicationchannels for communication with the transaction processing server 712for the transmission of transaction messages and other data to and fromthe transaction processing server 712. In some embodiments, the paymentnetwork associated with the transaction processing server 712 may own oroperate each transaction processor such that the payment network maymaintain control over the communication of transaction messages to andfrom the transaction processing server 712 for network and informationalsecurity.

In step 736, the transaction processing server 712 may performvalue-added services for the payment transaction. Value-added servicesmay be services specified by the issuing financial institution 702 thatmay provide additional value to the issuing financial institution 702 orthe consumer 704 in the processing of payment transactions. Value-addedservices may include, for example, fraud scoring, transaction or accountcontrols, account number mapping, offer redemption, loyalty processing,etc. For instance, when the transaction processing server 712 receivesthe transaction, a fraud score for the transaction may be calculatedbased on the data included therein and one or more fraud scoringalgorithms and/or engines. In some instances, the transaction processingserver 712 may first identify the issuing financial institution 702associated with the transaction, and then identify any servicesindicated by the issuing financial institution 702 to be performed. Theissuing financial institution 702 may be identified, for example, bydata included in a specific data element included in the authorizationrequest, such as an issuer identification number. In another example,the issuing financial institution 702 may be identified by the primaryaccount number stored in the authorization request, such as by using aportion of the primary account number (e.g., a bank identificationnumber) for identification.

In step 738, the transaction processing server 712 may electronicallytransmit the authorization request to the issuing financial institution702. In some instances, the authorization request may be modified, oradditional data included in or transmitted accompanying theauthorization request as a result of the performance of value-addedservices by the transaction processing server 712. In some embodiments,the authorization request may be transmitted to a transaction processor(e.g., owned or operated by the transaction processing server 712)situated at the issuing financial institution 702 or an entityassociated thereof, which may forward the authorization request to theissuing financial institution 702.

In step 740, the issuing financial institution 702 may authorize thetransaction account for payment of the payment transaction. Theauthorization may be based on an available credit amount for thetransaction account and the transaction amount for the paymenttransaction, fraud scores provided by the transaction processing server712, and other considerations that will be apparent to persons havingskill in the relevant art. The issuing financial institution 702 maymodify the authorization request to include a response code indicatingapproval (e.g., or denial if the transaction is to be denied) of thepayment transaction. The issuing financial institution 702 may alsomodify a message type indicator for the transaction message to indicatethat the transaction message is changed to be an authorization response.In step 742, the issuing financial institution 702 may transmit (e.g.,via a transaction processor) the authorization response to thetransaction processing server 712.

In step 744, the transaction processing server 712 may forward theauthorization response to the acquiring financial institution 710 (e.g.,via a transaction processor). In step 746, the acquiring financialinstitution may generate a response message indicating approval ordenial of the payment transaction as indicated in the response code ofthe authorization response, and may transmit the response message to thegateway processor 708 using the standards and protocols set forth by thegateway processor 708. In step 748, the gateway processor 708 mayforward the response message to the merchant 706 using the appropriatestandards and protocols. In step 770, assuming the transaction wasapproved, the merchant 706 may then provide the products purchased bythe consumer 704 as part of the payment transaction to the consumer 704.

In some embodiments, once the process 700 has completed, payment fromthe issuing financial institution 702 to the acquiring financialinstitution 710 may be performed. In some instances, the payment may bemade immediately or within one business day. In other instances, thepayment may be made after a period of time, and in response to thesubmission of a clearing request from the acquiring financialinstitution 710 to the issuing financial institution 702 via thetransaction processing server 712. In such instances, clearing requestsfor multiple payment transactions may be aggregated into a singleclearing request, which may be used by the transaction processing server712 to identify overall payments to be made by whom and to whom forsettlement of payment transactions.

In some instances, the system may also be configured to perform theprocessing of payment transactions in instances where communicationpaths may be unavailable. For example, if the issuing financialinstitution is unavailable to perform authorization of the transactionaccount (e.g., in step 740), the transaction processing server 712 maybe configured to perform authorization of transactions on behalf of theissuing financial institution 702. Such actions may be referred to as“stand-in processing,” where the transaction processing server “standsin” as the issuing financial institution 702. In such instances, thetransaction processing server 712 may utilize rules set forth by theissuing financial institution 702 to determine approval or denial of thepayment transaction, and may modify the transaction message accordinglyprior to forwarding to the acquiring financial institution 710 in step744. The transaction processing server 712 may retain data associatedwith transactions for which the transaction processing server 712 standsin, and may transmit the retained data to the issuing financialinstitution 702 once communication is reestablished. The issuingfinancial institution 702 may then process transaction accountsaccordingly to accommodate for the time of lost communication.

In another example, if the transaction processing server 712 isunavailable for submission of the authorization request by the acquiringfinancial institution 710, then the transaction processor at theacquiring financial institution 710 may be configured to perform theprocessing of the transaction processing server 712 and the issuingfinancial institution 702. The transaction processor may include rulesand data suitable for use in making a determination of approval ordenial of the payment transaction based on the data included therein.For instance, the issuing financial institution 702 and/or transactionprocessing server 712 may set limits on transaction type, transactionamount, etc. that may be stored in the transaction processor and used todetermine approval or denial of a payment transaction based thereon. Insuch instances, the acquiring financial institution 710 may receive anauthorization response for the payment transaction even if thetransaction processing server 712 is unavailable, ensuring thattransactions are processed and no downtime is experienced even ininstances where communication is unavailable. In such cases, thetransaction processor may store transaction details for the paymenttransactions, which may be transmitted to the transaction processingserver 712 (e.g., and from there to the associated issuing financialinstitutions 702) once communication is reestablished.

In some embodiments, transaction processors may be configured to includea plurality of different communication channels, which may utilizemultiple communication cards and/or devices, to communicate with thetransaction processing server 712 for the sending and receiving oftransaction messages. For example, a transaction processor may becomprised of multiple computing devices, each having multiplecommunication ports that are connected to the transaction processingserver 712. In such embodiments, the transaction processor may cyclethrough the communication channels when transmitting transactionmessages to the transaction processing server 712, to alleviate networkcongestion and ensure faster, smoother communications. Furthermore, ininstances where a communication channel may be interrupted or otherwiseunavailable, alternative communication channels may thereby beavailable, to further increase the uptime of the network.

In some embodiments, transaction processors may be configured tocommunicate directly with other transaction processors. For example, atransaction processor at an acquiring financial institution 710 mayidentify that an authorization request involves an issuing financialinstitution 702 (e.g., via the bank identification number included inthe transaction message) for which no value-added services are required.The transaction processor at the acquiring financial institution 710 maythen transmit the authorization request directly to the transactionprocessor at the issuing financial institution 702 (e.g., without theauthorization request passing through the transaction processing server712), where the issuing financial institution 702 may process thetransaction accordingly.

The methods discussed above for the processing of payment transactionsthat utilize multiple methods of communication using multiplecommunication channels, and includes fail safes to provide for theprocessing of payment transactions at multiple points in the process andat multiple locations in the system, as well as redundancies to ensurethat communications arrive at their destination successfully even ininstances of interruptions, may provide for a robust system that ensuresthat payment transactions are always processed successfully with minimalerror and interruption. This advanced network and its infrastructure andtopology may be commonly referred to as “payment rails,” wheretransaction data may be submitted to the payment rails from merchants atmillions of different points of sale, to be routed through theinfrastructure to the appropriate transaction processing servers 712 forprocessing. The payment rails may be such that a general purposecomputing device may be unable to properly format or submitcommunications to the rails, without specialized programming and/orconfiguration. Through the specialized purposing of a computing device,the computing device may be configured to submit transaction data to theappropriate entity (e.g., a gateway processor 708, acquiring financialinstitution 710, etc.) for processing using this advanced network, andto quickly and efficiently receive a response regarding the ability fora consumer 704 to fund the payment transaction.

Computer System Architecture

FIG. 8 illustrates a computer system 800 in which embodiments of thepresent disclosure, or portions thereof, may be implemented ascomputer-readable code. For example, the processing server of FIG. 1 maybe implemented in the computer system 800 using hardware, software,firmware, non-transitory computer readable media having instructionsstored thereon, or a combination thereof and may be implemented in oneor more computer systems or other processing systems. Hardware,software, or any combination thereof may embody modules and componentsused to implement the methods of FIGS. 3-7.

If programmable logic is used, such logic may execute on a commerciallyavailable processing platform configured by executable software code tobecome a specific purpose computer or a special purpose device (e.g.,programmable logic array, application-specific integrated circuit,etc.). A person having ordinary skill in the art may appreciate thatembodiments of the disclosed subject matter can be practiced withvarious computer system configurations, including multi-coremultiprocessor systems, minicomputers, mainframe computers, computerslinked or clustered with distributed functions, as well as pervasive orminiature computers that may be embedded into virtually any device. Forinstance, at least one processor device and a memory may be used toimplement the above described embodiments.

A processor unit or device as discussed herein may be a singleprocessor, a plurality of processors, or combinations thereof. Processordevices may have one or more processor “cores.” The terms “computerprogram medium,” “non-transitory computer readable medium,” and“computer usable medium” as discussed herein are used to generally referto tangible media such as a removable storage unit 818, a removablestorage unit 822, and a hard disk installed in hard disk drive 812.

Various embodiments of the present disclosure are described in terms ofthis example computer system 800. After reading this description, itwill become apparent to a person skilled in the relevant art how toimplement the present disclosure using other computer systems and/orcomputer architectures. Although operations may be described as asequential process, some of the operations may in fact be performed inparallel, concurrently, and/or in a distributed environment, and withprogram code stored locally or remotely for access by single ormulti-processor machines. In addition, in some embodiments the order ofoperations may be rearranged without departing from the spirit of thedisclosed subject matter.

Processor device 804 may be a special purpose or a general purposeprocessor device specifically configured to perform the functionsdiscussed herein. The processor device 804 may be connected to acommunications infrastructure 806, such as a bus, message queue,network, multi-core message-passing scheme, etc. The network may be anynetwork suitable for performing the functions as disclosed herein andmay include a local area network (LAN), a wide area network (WAN), awireless network (e.g., Wi-Fi), a mobile communication network, asatellite network, the Internet, fiber optic, coaxial cable, infrared,radio frequency (RF), or any combination thereof. Other suitable networktypes and configurations will be apparent to persons having skill in therelevant art. The computer system 800 may also include a main memory 808(e.g., random access memory, read-only memory, etc.), and may alsoinclude a secondary memory 810. The secondary memory 810 may include thehard disk drive 812 and a removable storage drive 814, such as a floppydisk drive, a magnetic tape drive, an optical disk drive, a flashmemory, etc.

The removable storage drive 814 may read from and/or write to theremovable storage unit 818 in a well-known manner. The removable storageunit 818 may include a removable storage media that may be read by andwritten to by the removable storage drive 814. For example, if theremovable storage drive 814 is a floppy disk drive or universal serialbus port, the removable storage unit 818 may be a floppy disk orportable flash drive, respectively. In one embodiment, the removablestorage unit 818 may be non-transitory computer readable recordingmedia.

In some embodiments, the secondary memory 810 may include alternativemeans for allowing computer programs or other instructions to be loadedinto the computer system 800, for example, the removable storage unit822 and an interface 820. Examples of such means may include a programcartridge and cartridge interface (e.g., as found in video gamesystems), a removable memory chip (e.g., EEPROM, PROM, etc.) andassociated socket, and other removable storage units 822 and interfaces820 as will be apparent to persons having skill in the relevant art.

Data stored in the computer system 800 (e.g., in the main memory 808and/or the secondary memory 810) may be stored on any type of suitablecomputer readable media, such as optical storage (e.g., a compact disc,digital versatile disc, Blu-ray disc, etc.) or magnetic tape storage(e.g., a hard disk drive). The data may be configured in any type ofsuitable database configuration, such as a relational database, astructured query language (SQL) database, a distributed database, anobject database, etc. Suitable configurations and storage types will beapparent to persons having skill in the relevant art.

The computer system 800 may also include a communications interface 824.The communications interface 824 may be configured to allow software anddata to be transferred between the computer system 800 and externaldevices. Exemplary communications interfaces 824 may include a modem, anetwork interface (e.g., an Ethernet card), a communications port, etc.Software and data transferred via the communications interface 824 maybe in the form of signals, which may be electronic, electromagnetic,optical, or other signals as will be apparent to persons having skill inthe relevant art. The signals may travel via a communications path 826,which may be configured to carry the signals and may be implementedusing wire, cable, fiber optics, a phone line, a cellular phone link, aradio frequency link, etc.

The computer system 800 may further include a display interface 802. Thedisplay interface 802 may be configured to allow data to be transferredbetween the computer system 800 and external display 830. Exemplarydisplay interfaces 802 may include high-definition multimedia interface(HDMI), digital visual interface (DVI), video graphics array (VGA), etc.The display 830 may be any suitable type of display for displaying datatransmitted via the display interface 802 of the computer system 800,including a cathode ray tube (CRT) display, liquid crystal display(LCD), light-emitting diode (LED) display, capacitive touch display,thin-film transistor (TFT) display, etc.

Computer program medium and computer usable medium may refer tomemories, such as the main memory 808 and secondary memory 810, whichmay be memory semiconductors (e.g., DRAM, etc.). These computer programproducts may be means for providing software to the computer system 800.Computer programs (e.g., computer control logic) may be stored in themain memory 808 and/or the secondary memory 810. Computer programs mayalso be received via the communications interface 824. Such computerprograms, when executed, may enable computer system 800 to implement thepresent methods as discussed herein. In particular, the computerprograms, when executed, may enable processor device 804 to implementthe methods illustrated by FIGS. 3-7, as discussed herein. Accordingly,such computer programs may represent controllers of the computer system800. Where the present disclosure is implemented using software, thesoftware may be stored in a computer program product and loaded into thecomputer system 800 using the removable storage drive 814, interface820, and hard disk drive 812, or communications interface 824.

The processor device 804 may comprise one or more modules or enginesconfigured to perform the functions of the computer system 800. Each ofthe modules or engines may be implemented using hardware and, in someinstances, may also utilize software, such as corresponding to programcode and/or programs stored in the main memory 808 or secondary memory810. In such instances, program code may be compiled by the processordevice 804 (e.g., by a compiling module or engine) prior to execution bythe hardware of the computer system 800. For example, the program codemay be source code written in a programming language that is translatedinto a lower level language, such as assembly language or machine code,for execution by the processor device 804 and/or any additional hardwarecomponents of the computer system 800. The process of compiling mayinclude the use of lexical analysis, preprocessing, parsing, semanticanalysis, syntax-directed translation, code generation, codeoptimization, and any other techniques that may be suitable fortranslation of program code into a lower level language suitable forcontrolling the computer system 800 to perform the functions disclosedherein. It will be apparent to persons having skill in the relevant artthat such processes result in the computer system 800 being a speciallyconfigured computer system 800 uniquely programmed to perform thefunctions discussed above.

Techniques consistent with the present disclosure provide, among otherfeatures, systems and methods for location scoring in a transactionbased on item redemption. While various exemplary embodiments of thedisclosed system and method have been described above it should beunderstood that they have been presented for purposes of example only,not limitations. It is not exhaustive and does not limit the disclosureto the precise form disclosed. Modifications and variations are possiblein light of the above teachings or may be acquired from practicing ofthe disclosure, without departing from the breadth or scope.

What is claimed is:
 1. A method of providing responses to a naturallanguage query, comprising: receiving a natural language query from amerchant, the merchant being associated with a merchant identifier;processing the natural language query to derive at least one intent andat least one entity; retrieving a plurality of transaction data entries,each transaction data entry including transaction data associated with apayment transaction having the merchant identifier associated with themerchant; identifying a set of the plurality of transaction data entriesthat correspond to the at least one derived intent or the at least oneentity; calculating a response to the natural language query based onthe set of the plurality of transaction data entries using at least oneanalytical model; generating a natural language response, based on thecalculated response, to the natural language query; and transmitting thegenerated natural language response.
 2. The method of claim 1, furthercomprising: retrieving a plurality of external information associatedwith the at least derived one intent or the at least one entity from atleast one external data source; and identifying a set of the pluralityof external information that correspond to the at least one derivedintent or the at least one entity; wherein the calculated response isfurther based on the set of the plurality of external information. 3.The method of claim 1, wherein the at least one derived intent includesa scheduled task for monitoring the plurality of transaction dataentries, the scheduled task having at least one triggering threshold;wherein the identified set of the plurality of transaction data entriesis updated based on the triggering threshold; and wherein the calculatedresponse is updated based on the triggering threshold and the updatedidentified set of the plurality of transactions.
 4. The method of claim1, wherein the derived intent is a request for explaining the generatednatural language response; and transmitting the at least one analyticalmodel.
 5. The method of claim 1, further comprising retrieving anotherplurality of transaction data entries, each transaction data entryincluding transaction data associated with a payment transaction nothaving the merchant identifier associated with the merchant.
 6. Themethod of claim 5, wherein the calculated response is further based onthe another plurality of external information, the calculated responseremoving identifying information from the another plurality of externalinformation.
 7. The method of claim 2, where the at least one datasource includes merchant accounting software executing on a merchantserver.
 8. The method of claim 1, wherein the at least one derivedintent includes one of a performance metric, a comparison metric, atimeframe, and a target user.
 9. The method of claim 1, furthercomprising: identifying a prediction of future performance related tothe at least one derived intent, wherein the natural language responseincludes the prediction of future performance.
 10. The method of claim9, wherein the prediction of future performance is identified by anartificial intelligence.
 11. A system providing responses to a naturallanguage query, comprising: a computing device configured to input intoan interface a natural language query created by a merchant; and aprocessing server comprising: a receiving device configured to receivethe natural language query being input into the computing device beingused by the merchant, the merchant being associated with a merchantidentifier; a natural language processor configured to derive at leastone intent and at least one entity; a querying module configured toretrieve a plurality of transaction data entries, each transaction dataentry including transaction data associated with a payment transactionhaving the merchant identifier associated with the merchant; thequerying module being further configured to identify a set of theplurality of transaction data entries that correspond to the at leastone derived intent or the at least one entity; a calculation moduleconfigured to calculate a response to the natural language query basedon the set of the plurality of transaction data entries using at leastone analytical model; a natural language response module configured togenerate a natural language response, based on the calculated response,to the natural language query; and a transmitting device configured totransmit the generated natural language response.
 12. The system ofclaim 11, wherein the querying module is further configured to: retrievea plurality of external information associated with the at least derivedone intent or the at least one entity from at least one external datasource; identify a set of the plurality of external information thatcorrespond to the at least one derived intent or the at least oneentity; and wherein the calculation module is further configured tocalculate the response based on the set of the plurality of externalinformation.
 13. The system of claim 11, wherein the natural languageprocessor derived intent includes a scheduled task for monitoring theplurality of transaction data entries, the scheduled task having atleast one triggering threshold; wherein the querying module updates theidentified set of the plurality of transaction data entries based on thetriggering threshold; and wherein the calculation module updates thecalculated response based on the triggering threshold and the updatedidentified set of the plurality of transactions.
 14. The system of claim11, wherein the receiving device receives a request from the computingdevice of the merchant to explain the generated natural languageresponse; wherein the natural language processor determines the derivedintent is a request for explaining the generated natural languageresponse; and wherein the transmitting device transmits the at least oneanalytical model used by the calculation module to generate the responseon which the generated natural language response is based.
 15. Thesystem of claim 11, wherein the querying module is further configured toretrieve another plurality of transaction data entries, each transactiondata entry including transaction data associated with a paymenttransaction not having the merchant identifier associated with themerchant; and wherein the calculation module calculated response isfurther based on the another plurality of external information.
 16. Thesystem of claim 15, wherein the querying module is further configured toremove identifying information from the another plurality of transactiondata entries.
 17. The system of claim 12, where the at least one datasource includes merchant accounting software executing on a merchantserver.
 18. The system of claim 11, wherein the at least one derivedintent includes one of a performance metric, a comparison metric, atimeframe, and a target user.
 19. The system of claim 11, furthercomprising: a predictive module configured to identify a prediction offuture performance related to the at least one derived intent, whereinthe natural language response includes the prediction of futureperformance.
 20. The system of claim 19, wherein the prediction offuture performance is identified by an artificial intelligence.
 21. Amethod for providing a proactive alert as a natural language message,comprising: storing, in a transaction database of a processing server, aplurality of transaction data entries, each transaction data entry beingrelated to a payment transaction involving a merchant and includingtransaction data; storing, in a memory of the processing server, aplurality of metrics, wherein each of the plurality of metrics isrelated to at least one of: merchant performance and market demographicsfor at least one of: the merchant, a geographic area including themerchant, a related merchant, and a market industry related to themerchant; identifying, by a calculation module of the processing server,a present metric for the merchant based on at least the transaction dataincluded in one or more transaction data entries stored in thetransaction database, wherein the present metric is related to merchantperformance or market demographics for the merchant and is differentfrom one of the plurality of metrics stored in the memory by at leastpredetermined threshold; generating, by a natural language responsemodule of the processing server, a natural language response includingat least the identified present metric and a difference between theidentified present metric and the one of the plurality of metrics; andelectronically transmitting, by a transmitting device of the processingserver, the generated natural language response to a computing systemassociated with the merchant.
 22. A system for providing a proactivealert as a natural language message, comprising: a transaction databaseof a processing server configured to store a plurality of transactiondata entries, each transaction data entry being related to a paymenttransaction involving a merchant and including transaction data; amemory of the processing server configured to store a plurality ofmetrics, wherein each of the plurality of metrics is related to at leastone of: merchant performance and market demographics for at least oneof: the merchant, a geographic area including the merchant, a relatedmerchant, and a market industry related to the merchant; a calculationmodule of the processing server configured to identify a present metricfor the merchant based on at least the transaction data included in oneor more transaction data entries stored in the transaction database,wherein the present metric is related to merchant performance or marketdemographics for the merchant and is different from one of the pluralityof metrics stored in the memory by at least predetermined threshold; anatural language response module of the processing server configured togenerate a natural language response including at least the identifiedpresent metric and a difference between the identified present metricand the one of the plurality of metrics; and a transmitting device ofthe processing server configured to electronically transmit thegenerated natural language response to a computing system associatedwith the merchant.